The application of RBF neural network sliding mode control on parallel robot

Abstract

The parallel robot has a complex system,the strong coupling and nonlinear characteristics.The sliding mode control is not sensitive to uncertainly parameter and external disturbance,which dose not need accurate mathematical mode of controlled object and the process of sliding mode controller is a natural decoupling process.This method is applicable to parallel robot control,but which has the shortcoming of chattering.In view of that,this paper proposes a control method that combines RBF neural network with sliding mode control.By the use of RBF neural network to adjust the sliding mode control's gain of the switching,which effectively weaken the chattering and obtain the good control effect.The simulation results show that the control method has good tracking performance,small system error and strong robustness,and can satisfy the requirements of the parallel robot control.